leonardBang commented on code in PR #800:
URL: https://github.com/apache/flink-web/pull/800#discussion_r2234502885
##########
docs/data/release_archive.yml:
##########
@@ -1,5 +1,9 @@
release_archive:
flink:
+ -
+ version_short: "2.0"
Review Comment:
```suggestion
version_short: "2.1"
```
##########
docs/content/posts/2025-07-31-release-2.1.0.md:
##########
@@ -0,0 +1,528 @@
+---
+authors:
+ - reswqa:
+ name: "Ron Liu"
+ twitter: "Ron999"
+
+date: "2025-07-31T08:00:00Z"
+subtitle: ""
+title: Ushers in a New Era of Unified Real-Time Data + AI with Comprehensive
Upgrades
+aliases:
+ - /news/2025/07/31/release-2.1.0.html
+---
+
+The Apache Flink PMC is proud to announce the release of Apache Flink 2.1.0.
This marks a significant milestone
+in the evolution of the real-time data processing engine into a unified Data +
AI platform. This release brings
+together 116 global contributors, implements 16 FLIPs (Flink Improvement
Proposals), and resolves over 220 issues,
+with a strong focus on deepening the integration of real-time AI and
intelligent stream processing:
+
+1. **Breakthroughs in Real-Time AI**:
+ - Introduces AI Model DDL, enabling flexible management of AI models
through Flink SQL and the Table API.
+
+ - Extends the `ML_PREDICT` Table-Valued Function (TVF), empowering
real-time invocation of AI models within Flink SQL,
+ laying the foundation for building end-to-end real-time AI workflows.
+
+2. **Enhanced Real-Time Data Processing**:
+ - Process Table Functions (PTFs) open up the Flink SQL engine for more
event-driven application.
+ Giving access to Flink’s managed state, event-time and timer services,
and underlying table changelogs.
+
+ - Adds the `VARIANT` data type for efficient handling of semi-structured
data like JSON. Combined with the `PARSE_JSON` function
+ and lakehouse formats (e.g., Apache Paimon), it enables dynamic schema
data analysis.
+
+ - Significantly optimizes streaming joins with the innovative introduction
of `DeltaJoin` and `MultiJoin` strategies,
+ eliminating state bottlenecks and improving resource utilization and job
stability.
+
+Flink 2.1.0 seamlessly integrates real-time data processing with AI models,
empowering enterprises to advance from
+real-time analytics to real-time intelligent decision-making, meeting the
evolving demands of modern data applications.
+We extend our gratitude to all contributors for their invaluable support!
+
+Let's dive into the highlights.
+
+# Flink SQL Improvements
+
+## Model DDLs
+Since Flink 2.0, we have introduced dedicated syntax for AI models, enabling
users to define models
+as easily as creating catalog objects and invoke them like standard functions
or table functions in SQL statements.
+In Flink 2.1, we have also added Model DDLs Table API support, enabling users
to define and manage AI models programmatically
+via the Table API in both Java and Python. This provides a flexible,
code-driven alternative to SQL for model management and
+integration within Flink applications.
+
+Example:
+- Defining a Model via Flink SQL
+```sql
+CREATE MODEL my_model
+INPUT (f0 STRING)
+OUTPUT (label STRING)
+WITH (
+ 'task' = 'classification',
+ 'type' = 'remote',
+ 'provider' = 'openai',
+ 'openai.endpoint' = 'remote',
+ 'openai.api_key' = 'abcdefg',
+);
+```
+
+- Defining a Model via Table API (Java)
+```java
+tEnv.createModel(
+ "MyModel",
+ ModelDescriptor.forProvider("OPENAI")
+ .inputSchema(Schema.newBuilder()
+ .column("f0", DataTypes.STRING())
+ .build())
+ .outputSchema(Schema.newBuilder()
+ .column("label", DataTypes.STRING())
+ .build())
+ .option("task", "classification")
+ .option("type", "remote")
+ .option("provider", "openai")
+ .option("openai.endpoint", "remote")
+ .option("openai.api_key", "abcdefg")
+ .build(),
+ true);
+```
+
+**More Information**
+* [FLINK-37548](https://issues.apache.org/jira/browse/FLINK-37548)
+*
[FLIP-437](https://cwiki.apache.org/confluence/display/FLINK/FLIP-437%3A+Support+ML+Models+in+Flink+SQL)
+*
[FLIP-507](https://cwiki.apache.org/confluence/display/FLINK/FLIP-507%3A+Add+Model+DDL+methods+in+TABLE+API)
+
+## Realtime AI Function
+
+Based on the AI model DDL, In Flink 2.1, we expanded the `ML_PREDICT`
table-valued function (TVF) to perform realtime model inference in SQL queries,
applying machine learning models to data streams seamlessly.
+The implementation supports both Flink builtin model providers (OpenAI) and
interfaces for users to define custom model providers, accelerating Flink's
evolution from a real-time
+data processing engine to a unified realtime AI platform. Looking ahead, we
plan to introduce more AI functions such as `ML_EVALUATE`, `VECTOR_SEARCH` to
unlock end-to-end experience
+for real-time data processing, model training, and inference.
+
+Take the following SQL statements as an example:
+```sql
+-- Declare a AI model
+CREATE MODEL `my_model`
+INPUT (text STRING)
+OUTPUT (response STRING)
+WITH(
+ 'provider' = 'openai',
+ 'endpoint' = 'https://api.openai.com/v1/llm/v1/chat',
+ 'api-key' = 'abcdefg',
+ 'system-prompt' = 'translate to Chinese',
+ 'model' = 'gpt-4o'
+);
+
+-- Basic usage
+SELECT * FROM ML_PREDICT(
+ TABLE input_table,
+ MODEL my_model,
+ DESCRIPTOR(text)
+);
+
+-- With configuration options
+SELECT * FROM ML_PREDICT(
+ TABLE input_table,
+ MODEL my_model,
+ DESCRIPTOR(text)
+ MAP['async', 'true', 'timeout', '100s']
+);
+
+-- Using named parameters
+SELECT * FROM ML_PREDICT(
+ INPUT => TABLE input_table,
+ MODEL => MODEL my_model,
+ ARGS => DESCRIPTOR(text),
+ CONFIG => MAP['async', 'true']
+);
+```
+
+**More Information**
+* [FLINK-34992](https://issues.apache.org/jira/browse/FLINK-34992)
+* [FLINK-37777](https://issues.apache.org/jira/browse/FLINK-37777)
+*
[FLIP-437](https://cwiki.apache.org/confluence/display/FLINK/FLIP-437%3A+Support+ML+Models+in+Flink+SQL)
+*
[FLIP-525](https://cwiki.apache.org/confluence/display/FLINK/FLIP-525%3A+Model+ML_PREDICT%2C+ML_EVALUATE+Implementation+Design)
+* [Model
Inference](https://nightlies.apache.org/flink/flink-docs-release-2.1/docs/dev/table/sql/queries/model-inference/)
+
+## Process Table Functions (PTFs)
+
+Apache Flink now includes support for Process Table Functions (PTFs), the most
powerful function kind for Flink SQL and Table API.
+
+Conceptually, a PTF is a superset of all other user-defined functions, mapping
zero, one, or multiple tables to zero, one, or multiple rows.
+They enable implementing user-defined operators that can be as feature-rich as
built-in operations. PTFs have access to Flink's managed state,
+event-time, timer services, and table changelogs.
+
+PTFs enable the following tasks:
+- Apply transformations on each row of a table.
+- Logically partition the table into distinct sets and apply transformations
per set.
+- Store seen events for repeated access.
+- Continue the processing at a later point in time enabling waiting,
synchronization, or timeouts.
+- Buffer and aggregate events using complex state machines or rule-based
conditional logic.
+
+This moves Flink SQL significantly closer to the DataStream API, leveraging
the robustness and familiarity of the existing SQL ecosystem.
+Detailed information on PTF syntax and semantics can be found here: [Process
Table
Functions](https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/functions/ptfs/).
+
+Take the following code as an example:
+```java
+// Declare a ProcessTableFunction for memorizing your customers
+public static class GreetingWithMemory extends ProcessTableFunction<String> {
+ public static class CountState {
+ public long counter = 0L;
+ }
+
+ public void eval(@StateHint CountState state,
@ArgumentHint(SET_SEMANTIC_TABLE) Row input) {
+ state.counter++;
+ collect("Hello " + input.getFieldAs("name") + ", your " +
state.counter + " time?");
+ }
+}
+
+TableEnvironment env = TableEnvironment.create(...);
+
+// Call the PTF in Table API
+env.fromValues("Bob", "Alice", "Bob")
+ .as("name")
+ .partitionBy($("name"))
+ .process(GreetingWithMemory.class)
+ .execute()
+ .print();
+
+// Call the PTF in SQL
+env.executeSql("SELECT * FROM GreetingWithMemory(TABLE Names PARTITION BY
name)")
+ .print();
+```
+
+**More Information**
+*
[FLIP-440](https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=298781093)
+
+## Variant Type
+
+Variant is a new data type for semi-structured data(e.g. JSON), it supports
storing any
+semi-structured data, including ARRAY, MAP(with STRING keys), and scalar
types—while preserving
+field type information in a JSON-like structure. Unlike ROW and STRUCTURED
types, VARIANT provides
+superior flexibility for handling deeply nested and evolving schemas.
+
+Users can use `PARSE_JSON` or`TRY_PARSE_JSON` to convert JSON-formatted
VARCHAR data to VARIANT. In
+addition, table formats like Apache Paimon now support the VARIANT type, this
enable
+users to efficiently process semi-structured data in lakehouse using Flink SQL.
+
+Take the following SQL statements as an example:
+```sql
+CREATE TABLE t1 (
+ id INTEGER,
+ v STRING -- a json string
+) WITH (
+ 'connector' = 'mysql-cdc',
+ ...
+);
+
+CREATE TABLE t2 (
+ id INTEGER,
+ v VARIANT
+) WITH (
+ 'connector' = 'paimon'
+ ...
+);
+
+-- write to t2 with VARIANT type
+INSERT INTO t2 SELECT id, PARSE_JSON(v) FROM t1;
+```
+
+**More Information**
+* [FLINK-37922](https://issues.apache.org/jira/browse/FLINK-37922)
+*
[FLIP-521](https://cwiki.apache.org/confluence/display/FLINK/FLIP-521%3A+Integrating+Variant+Type+into+Flink%3A+Enabling+Efficient+Semi-Structured+Data+Processing)
+*
[Variant](https://nightlies.apache.org/flink/flink-docs-release-2.1/docs/dev/table/types/#other-data-types)
+
+## Structured Type Enhancements
+
+In Flink 2.1, we enabled declare user-defined objects via STRUCTURED TYPE
directly in `CREATE TABLE` DDL
+statements, resolving critical type equivalence issues and significantly
improving API usability.
+
+Take the following SQL statements as an example:
+```sql
+CREATE TABLE MyTable (
+ uid BIGINT,
+ user STRUCTURED<'com.example.User', name STRING, age INT NOT NULL>
+);
+
+-- Casts a row type into a structured type
+INSERT INTO MyTable SELECT 1, CAST(('Bob', 42) AS
STRUCTURED<'com.example.User', name STRING, age INT>);
+```
+
+**More Information**
+* [FLINK-37861](https://issues.apache.org/jira/browse/FLINK-37861)
+*
[FLIP-520](https://cwiki.apache.org/confluence/display/FLINK/FLIP-520%3A+Simplify+StructuredType+handling)
+*
[STRUCTURED](https://nightlies.apache.org/flink/flink-docs-release-2.1/docs/dev/table/types/#user-defined-data-types)
+
+## Delta Join
+
+Introduced a new DeltaJoin operator in stream processing jobs, along with
optimizations for simple
+streaming join pipeline. Compared to traditional streaming join, delta join
requires significantly
+less state, effectively mitigating issues related to large state, including
resource bottlenecks,
+slow checkpointing, and lengthy job recovery times. This feature is enabled by
default.
+
+**More Information**
+* [FLINK-37836](https://issues.apache.org/jira/browse/FLINK-37836)
+* [Delta
Join](https://cwiki.apache.org/confluence/display/FLINK/FLIP-486%3A+Introduce+A+New+DeltaJoin)
+
+## Multiple Regular Joins
+
+Streaming Flink jobs with multiple cascaded streaming joins often experience
operational
+instability and performance degradation due to large state sizes. This release
introduces a
+multi-join operator (`StreamingMultiJoinOperator`) that drastically reduces
state size
+by eliminating intermediate results. The operator achieves this by processing
joins across all input
+streams simultaneously within a single operator instance, storing only raw
input records instead of
+propagated join output.
+
+This "zero intermediate state" approach primarily targets state reduction,
offering substantial
+benefits in resource consumption and operational stability. This feature is
now available for
+pipelines with multiple INNER/LEFT joins that share at least one common join
key, enable with
+`SET 'table.optimizer.multi-join.enabled' = 'true'`.
+
+**Benchmark**: we conducted a benchmark comparing the benefits of the
multi-join operator with default binary joins, more detail can see
+[MultiJoin
Benchmark](https://nightlies.apache.org/flink/flink-docs-release-2.1/docs/dev/table/tuning/#multiple-regular-joins).
+
+
+**More Information**
+* [FLINK-37859](https://issues.apache.org/jira/browse/FLINK-37859)
+*
[MultiJoin](https://cwiki.apache.org/confluence/display/FLINK/FLIP-516%3A+Multi-Way+Join+Operator)
+
+## Async Lookup Join Enhancements
+
+In previous versions of async lookup join, even if users set
`table.exec.async-lookup.output-mode` to `ALLOW_UNORDERED`,
+the engine would still forcibly fallback to ordered mode when processing
update streams to ensure correctness.
+Starting from Flink 2.1, the engine allows parallel processing of unrelated
update records while still ensuring correctness,
+thereby achieving higher throughput when handling changelog streams.
+
+**More Information**
+* [FLINK-37874](https://issues.apache.org/jira/browse/FLINK-37874)
+* [Async Lookup
Join](https://cwiki.apache.org/confluence/display/FLINK/FLIP-519%3A++Introduce+async+lookup+key+ordered+mode)
+
+## Sink Reuse
+
+Within a single Flink job, when writing multiple `INSERT INTO` statements
updating identical columns (
+different columns will be supported in next release) of a target table, the
planner will optimize
+the execution plan and merge the sink nodes to achieve reuse. This will be a
great usability improvement
+for users using partial-update features with data lake storages like Apache
Paimon.
+
+**More Information**
+* [FLINK-37227](https://issues.apache.org/jira/browse/FLINK-37227)
+* [Sink
Reuse](https://cwiki.apache.org/confluence/display/FLINK/FLIP-506%3A+Support+Reuse+Multiple+Table+Sinks+in+Planner)
+
+## Support Smile Format for Compiled Plan Serialization
+
+In Flink 2.1, we added smile binary format support for compiled plans,
providing a memory-efficient
+alternative to JSON for serialization/deserialization. By default JSON is
used, in order to use
+smile format need to call `CompiledPlan#asSmileBytes` and
`PlanReference#fromSmileBytes` method.
+
+**More Information**
+* [FLINK-37341](https://issues.apache.org/jira/browse/FLINK-37341)
+* [Smile
Format](https://cwiki.apache.org/confluence/display/FLINK/FLIP-508%3A+Add+support+for+Smile+format+for+Compiled+plans)
+* [Smile Format
Specification](https://github.com/FasterXML/smile-format-specification/blob/master/smile-specification.md)
+
+# Runtime
+
+## Add Pluggable Batching for Async Sink
+
+In Flink 2.1, we introduced a pluggable batching mechanism for async sink that
allows users to define custom
+batching write strategies tailored to specific requirements.
+
+**More Information**
+* [FLINK-37298](https://issues.apache.org/jira/browse/FLINK-37298)
+* [Pluggable Batching for Async
Sink](https://cwiki.apache.org/confluence/display/FLINK/FLIP-509+Add+pluggable+Batching+for+Async+Sink)
+
+## Split-level Watermark Metrics
+
+In Flink 2.1, we added some split level watermark metrics, covering watermark
progress and per-split state gauges
+to enhance the watermark observability:
+
+- `currentWatermark`: the last watermark this split has received.
+- `activeTimeMsPerSecond`: the time this split is active per second.
+- `pausedTimeMsPerSecond`: the time this split is paused due to watermark
alignment per second.
+- `idleTimeMsPerSecond`: the time this split is marked idle by idleness
detection per second.
+- `accumulatedActiveTimeMs`: accumulated time this split was active since
registered.
+- `accumulatedPausedTimeMs`: accumulated time this split was paused since
registered.
+- `accumulatedIdleTimeMs`: accumulated time this split was idle since
registered.
+
+**More Information**
+* [FLINK-37410](https://issues.apache.org/jira/browse/FLINK-37410)
+* [Watermark
Metrics](https://cwiki.apache.org/confluence/display/FLINK/FLIP-513%3A+Split-level+Watermark+Metrics)
+
+# Connectors
+
+## Introduce SQL Connector for Keyed State
+
+In Flink 2.1, we introduced a new connector for keyed state. This connector
allows
+users to query keyed state directly from checkpoint or savepoint using Flink
SQL, making it easier
+to inspect, debug, and validate the state of Flink jobs without custom
tooling. This feature is
+especially useful for analyzing long-running jobs and validating state
migrations.
+
+With a simple DDL, you can expose ValueState as table and run Flink SQL query
the snapshot:
+```sql
+CREATE TABLE keyed_state (
+ k INTEGER,
+ user_id STRING,
+ balance DOUBLE
+) WITH (
+ 'connector' = 'savepoint',
+ 'path' = 'file:///savepoint/path&',
+ 'uid' = 'my-operator-id'
+);
+
+-- Query the keyed state
+SELECT * FROM keyed_state;
+```
+
+**More Information**
+* [FLINK-36929](https://issues.apache.org/jira/browse/FLINK-36929)
+* [Savepoint
Connector](https://cwiki.apache.org/confluence/display/FLINK/FLIP-496%3A+SQL+connector+for+keyed+savepoint+data)
+
+# Others Improvements
+
+## PyFlink
+In PyFlink 2.1, we added support for Python 3.12 and removed Python 3.8.
+
+**More Information**
+* [FLINK-37823](https://issues.apache.org/jira/browse/FLINK-37823)
+* [FLINK-37776](https://issues.apache.org/jira/browse/FLINK-37776)
+
+
+## Upgrade flink-shaded version to 20.0
+
+Bump flink-shaded version to 20.0 to support Smile format.
+
+**More Information**
+* [FLINK-37376](https://issues.apache.org/jira/browse/FLINK-37376)
+
+## Upgrade Parquet version to 1.15.3
+
+Bump parquet version to 1.15.3 to resolve parquet-avro module
+vulnerability found in
[CVE-2025-30065](https://nvd.nist.gov/vuln/detail/CVE-2025-30065).
+
+**More Information**
+* [FLINK-37760](https://issues.apache.org/jira/browse/FLINK-37760)
+
+# Upgrade Notes
+
+The Flink community tries to ensure that upgrades are as seamless as possible.
+However, certain changes may require users to make adjustments to certain parts
+of the program when upgrading to version 2.1. Please refer to the
+[release
notes](https://nightlies.apache.org/flink/flink-docs-release-2.1/release-notes/flink-2.1/)
+for a comprehensive list of adjustments to make and issues to check during the
+upgrading process.
+
+# List of Contributors
+
+The Apache Flink community would like to express gratitude to all the
+contributors who made this release possible:
+
+Ahmed Hamdy,
+Alan Sheinberg,
+Aleksandr Iushmanov,
+Aleksandr Savonin,
+AlexYinHan,
+Ammu Parvathy,
+Anupam Aggarwal,
+Ao Li,
+Arvid Heise,
+Au-Miner,
+Benchao Li,
+Bonnie Varghese,
+Chris,
+David Moravek,
+David Radley,
+David Wang,
+Dawid Wysakowicz,
+Dian Fu,
+Efrat Levitan,
+Feng Jin,
+Ferenc Csaky,
+Francesco Di Chiara,
+Gabor Somogyi,
+Gunnar Morling,
+Gustavo de Morais,
+Hangxiang Yu,
+Hao Li,
Review Comment:
So many lines, could you collect these name to in one line ?
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